Goal
The goal of this lab is to become familiar with spectral reflectance signatures of different materials on and near the Earth's surface. Health monitoring of vegetation and soil will also be introduced using NDVI and ferrous mineral indices.
Methods
Part 1: Spectral Signature Analysis
Spectral signatures can be collected either in the field or from software like ERDAS Imagine. In this lab, the spectral signatures from the image below of Eau Claire and Chippewa counties will be taken using ERDAS Imagine.
Figure 1: Image from which spectral signatures are taken in this lab
First the polygon tool was used from the drawing menu to create a small polygon completely within the water (not including any land edges) of Lake Wissota. This created an area of interest (AOI) from which a signature could be taken shown in Figure 2.
Figure 2: Polygon created as AOI in open water of Lake Wissota
The "Supervised" window was opened and "Signature Editor" was selected. The "Create New Signature from AOI" was selected and the signature from the polygon above was automatically added to the list. The name and color were changed to reflect the source of the signature.
Figure 3: Signature Editor
This process was followed for running water, deciduous forest, riparian forest, coniferous forest, crops, dry soil, moist soil, rock, asphalt highway, airport runway, and parking lot for a total of 12 signatures.
To display a chart, "Display Mean Plot Window" was selected from the Signature Editor. Chart options were changed so that chart background was set to white for better visibility of the signature.
Part 2: Resource Monitoring
Section 1: Vegetation Health Monitoring
Vegetation health monitoring is often done using the Normalized Difference Vegetation Index (NDVI). The equation for NDVI is:
The NDVI for this lab was done on the image of Eau Claire and Chippewa Counties below:
Figure 3: Image of Eau Claire and Chippewa Counties used for NDVI
To use NDVI in ERDAS Imagine, it is selected from the dropdown menu for "Unsupervised" in the Raster tab. The specifications for this lab are shown in Figure 4.
Figure 4: Specifications for NDVI
A map was then created and symbolized appropriately in ArcMap, which can be seen in the results section.
Section 2: Soil Health Monitoring
Soil health monitoring is often done using the Ferrous Mineral Index (FM). The equation for FM is:
The same image in Figure 3 was also used for the Ferrous Mineral Index. To use FM in , it is selected from the dropdown menu for "Unsupervised" in the Raster tab. The specifications for this lab are shown in Figure 4.
Figure 5: Specifications for FM
A map was then created and symbolized appropriately in ArcMap, which can be seen in the results section.
Results
The spectral signatures for 12 individual features are shown below. Band 1 corresponds with blue, band 2 with green, band 3 with red, band 4 with NIR, and bands 5 and 6 with MIR.
Figure 6: Spectral signature of standing water
Figure 7: Spectral signature of running water
Figure 8: Spectral signature of deciduous forest
Figure 9: Spectral signature of coniferous forest
Figure 10: Spectral signature of riparian forest
Figure 11: Spectral signature of crops
Figure 12: Spectral signature of dry soil
Figure 13: Spectral signature of moist soil
Figure 14: Spectral signature of rock
Figure 15: Spectral signature of asphalt highway
Figure 16: Spectral signature of airport runway
Figure 17: Spectral signature of parking lot
In order to compare the spectral signatures of moist and dry soil, they were put onto the same chart using that option. As expected due to the high absorbance of water, there was higher reflectance in the dry soil.
Figure 18: Comparison of moist and dry soil spectral signatures
An even broader comparison was made of all 12 spectral signatures together. They follow what is to be expected. Standing and moving water vary slightly in the
visible spectrum but still share similar signatures that decrease in
reflectance as wavelength increases. Rock, airport runway, and dry soil have similar
signatures, probably because they are composed of similar materials. Riparian forest and deciduous forest are
similar, likely because the riparian forest contains mostly deciduous trees. Forest in general is unique in that its spectral signature is low in the visible and spikes
in NIR. All other surfaces tend to dip in the NIR, especially dry, rocky
surfaces. Water also has a
unique spectral signature that has some reflectance in the visible and dips to
almost no reflectance in NIR. Other signatures have more ups and downs,
especially in NIR. Athough similar, riparian/deciduous forest and coniferous forest are different due to the surface area of the
needles/leaves, the shape of the canopy, and the direction of the branches.
Figure 19: All 12 spectral signatures
The NDVI gives insight into areas of high and low vegetation, as seen by the map in Figure 13. Areas of high vegetation are brighter because compared to other spectral signatures, there is high NIR reflectance and low red reflectance. Areas that are medium gray and black have no or almost no vegetation. They are mostly water, which absorbs most of the NIR band.
Figure 13: NDVI map of Eau Claire and Chippewa Counties
The FM shows areas of high and low ferrous mineral content, shown by Figure 14. Most of the surface ferrous minerals are west of
Lake Wissota in Chippewa Falls and lie mainly in what looks like uncultivated
agricultural fields. Urban areas lack ferrous minerals on the surface and water
has low minerals.
Figure 14: Ferrous Mineral map of Eau Claire and Chippewa Counties
Sources
Earth Resources Observation and Science Center, United States Geological Survey. (2019). [Satalllite images of Eau Claire and Chippewa counties].
Wilson, C. (2019). Spectral signature analysis and resource monitoring. Geog 338: Remote Sensing of the Environment Lab 8.
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